Prediction of the potential distribution of the predatory mite Neoseiulus californicus (McGregor) in China under current and future climate scenarios

Neoseiulus californicus is a predatory mite with a wide global distribution that can effectively control a variety of pest mites. In this study, MaxEnt was used to analyse the potential distribution of N. californicus in China and the BCC-CSM2-MR model was used to predict changes in the suitable areas for the mite from 2021 to 2100 under the scenarios of SSP126, SSP245 and SSP585. The results showed that (1) the average of area under curve value of the model was over 0.95, which demonstrated an excellent model accuracy. (2) Annual mean temperature (Bio1), precipitation of coldest quarter (Bio19), and precipitation of driest quarter (Bio17) were the main climatic variables that affected and controlled the potential distribution of N. californicus, with suitable ranges of 6.97–23.27 °C, 71.36–3924.8 mm, and 41.94–585.08 mm, respectively. (3) The suitable areas for N. californicus were mainly distributed in the southern half of China, with a total suitable area of 226.22 × 104 km2 in current. Under the future climate scenario, compared with the current scenario, lowly and moderately suitable areas of N. californicus increased, while highly suitable areas decreased. Therefore, it may be necessary to cultivate high-temperature resistant strains of N. californicus to adapt to future environmental changes.

variables that had a great influence on the distribution of N. californicus was compared by the Jackknife method, and the results are shown in Fig. 1. The longer the blue band was, the more important the variable was to the distribution of the species. Combined with the contribution rate of these environmental variables to the species (Fig. 2), the three most important environmental variables for N. californicus were Bio1 (29.4%), Bio19 (15%), and Bio17 (16.4%).

Discussion
N. californicus, as a predatory mite, is widely distributed worldwide and can effectively control a variety of pest mites in orchards and vegetable fields. In agriculture, using predatory mites to control agricultural pest mites is an important measure of biological control that has a positive effect on the development of green agriculture. The climate is the largest factor affecting species distribution, and climate change has a great impact on biological    www.nature.com/scientificreports/ In this study, MaxEnt was used to construct a distribution model of N. californicus to determine the distribution of this mite in China under current climate conditions and to predict its distribution under different scenarios in the future. As a commonly used model for predicting species distribution, MaxEnt has a wide range of applications and still has good prediction results when species distribution points are small, their numbers are uncertain, or their correlations with environmental variables are unknown 39 . Tognelli et al. 40 found that MaxEnt had the highest accuracy of all tested models, especially for species sampled from relatively few sites, when determining the distribution of Patagonian insects by artificial neural networks, BIOCLIM, classification and regression trees, DOMAIN, generalized additive models, GARP, generalized linear models, and the MaxEnt model. Pangga et al. 41 used the MaxEnt model to accurately predict the species distribution of Aspidiotus rigidus Reyne, whose main environmental variables were annual temperature variation and seasonality. In addition, before using MaxEnt to predict the distribution, we used the ENMeval package in R to optimize the model and selected a model combination with delta AICc equal to 0 because this tuning exercise can result in a model with a balanced goodness of fit 42 . Ultimately, the AUC values of the distribution data simulated by MaxEnt were all greater than 0.95, so our model was considered robust and sufficient to explain the distribution of N. californicus.
Species distribution is highly susceptible to the influence of the environment, and the environment directly or indirectly affects the physiological and ecological functions of species, thereby limiting their distribution [43][44][45] . In this study, we combined the species distribution points of N. californicus and 19 environmental variables to simulate the current suitable distribution of the species with MaxEnt. Combining Jackknife tests and the contribution rate of selected environmental variables with the species locations, Bio1, Bio19 and Bio17 were found to be the main environmental variables affecting the suitable distribution of N. californicus. As the most important factor of species distribution, temperature limits the species distribution by affecting its effective accumulation and temperature at the developmental stage. Zhang et al. 46 reported that N. californicus can reproduce normally at 15-35 °C, with the highest net proliferation rate at 25 °C, and its generation growth cycle shortens with increasing temperature. Wang et al. 31 analysed the current suitable distribution of N. californicus and found that Bio19 had an important impact on the distribution of N. californicus and that environmental variables related to precipitation in April (prec4), precipitation in June (prec6), precipitation in October (prec10), and precipitation in December (prec12) had important effects on the distribution of N. californicus. Because mites are small in size, they are affected by factors such as wind and rain in addition to temperature, and rainfall can negatively affect them by drowning them or knocking them into the soil 47 . The scouring of heavy rain and torrential rain has been reported to have a significant inhibitory effect on T. cinnabarinus 48 . Our results indicated that the total suitable area for N. californicus was 226.22 × 10 4 km 2 . Of this area, the proportion of lowly suitable area accounted for 25.73%, moderately suitable area accounted for 34.08%, and highly suitable area accounted for 40.19%. In general, the boundaries of suitable and unsuitable areas for N. californicus were generally consistent with the findings of Wang et al. 31 In addition, there were also some differences in the locations and areas of different suitable areas. The reason for these differences may be that we obtained 118 points for our distribution prediction after removing redundant points, while Wang et al. obtained 65 points. We performed a model optimization for the parameters in R before running the MaxEnt fitness simulations 31 .
As the global climate warms, the structure and function of terrestrial ecosystems may be significantly altered, resulting in significant changes in the extent and distribution of biological habitats 49 . The latest CMIP6 model shows that the world will be significantly warmed in the future. In the future SSP126, SSP245, and SSP585 scenarios, the temperature is predicted to increase by 1.3-2.9 °C, 2.1-4.3 °C, and 3.8-7.4 °C, respectively 50 . Therefore, based on the current distribution of N. californicus, we predicted the potential redistribution of N. californicus in response to climate change under these three climate scenarios in the twenty-first century. Our results showed that the size and distribution of the areas of N. californicus were different under the three future climate scenarios, but all showed basically the same trends. Compared with the current distribution, the distribution areas of the lowly and moderately of N. californicus showed an upwards trend (except in the 2090s under the SSP585  51 . This may be one reason why the area of highly suitable area for N. californicus was projected to decrease in the future due to climate change. In addition, due to climate change, the overall migration trend of N. californicus was to the west (SSP126), northwest (SSP245), and northeast (SSP585). Climate change and the frequent occurrence of extreme weather will restrict the continuous control of phytoseiid mites in agricultural ecosystems and high-temperature will often interfere with the biological control of tetranychus mites using phytoseius mites. Yuan et al. 52 found that high-temperature exposure had significant effects on the egg hatching rate, survival rate and development duration of N. californicus, but had little effect on the pre-oviposition and survival rate of the adults.Thus, to adapt to the future climate and continue to effectively and continuously control pests and mites in a high-temperature environment, high-temperature resistant strains of N. californicus may need to be cultivated in the future as Zhang et al. 53 selected the high-temperature resistant strain of Neoseiulus barkeri.
Climatic variables related to N. californicus. Historical and future climate data were downloaded from the WorldClim website (https:// www. world clim. org/) and included 19 bioclimatic variables (2.5′ resolution) ( Table 5). The historical climate data were the average values from 1970 to 2000. The future climate data (2030s, 2050s, 2070s and 2090s) were from three different scenarios SSP126, SSP245 and SSP585 under BCC-CSM2-MR mode. Second, the correlation analysis of 19 environmental variables (historical climate data) was carried out by using ENMtools software and use R package "corrplot" 67 to draw heat map, and the contribution rate analysis was carried out by using MaxEnt software to import species data and environmental data. Environmental variables were determined to be suitable based on Pearson's coefficients higher than |0.8| (very significant correlation) (Fig. 9) and contribution rates (Fig. 10).
After completing the above steps, six environmental variables were finally retained and used to build the final model, including annual mean temperature (Bio1), mean diurnal range (Bio2), temperature seasonality (Bio4), annual precipitation (Bio12), precipitation of driest quarter (Bio17), and precipitation of coldest quarter (Bio19).   68 in R was used to test the Akaike information criterion correction (AICc), which generally provides priority to parameters with small AICc values for simulation and is considered to be a standard measure of the goodness of fit of a model. Finally, we selected the model with a delta AICc equal to 0 according to the result of the ENMeval procedure.
Distribution modelling with MaxEnt. The occurrence data and seven environmental variables of N. californicus were input into MaxEnt, and then 'Create Response Curve' , 'Do Jackknife to Measure Variable Importance' , and output format as 'Logistic' were selected. In the settings, the 'Random test percentage' was set to 25 (75% distribution points were randomly selected as the training set to build the model, and the remaining 25% distribution points were selected as the test set), and the 'Regularization Multiplier' was set to 1, 'Replicates' was set to 10 times; the model was set to 'Hinge Features' and the other parameters were set to the default software   www.nature.com/scientificreports/